# # # # # Section 02 - Import dataset ---------------------------------------- #+++---my_dataset <- _+A+_my_import_sentence_+A+_ head (x = my_dataset, n = 5 )
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
# # # # # Section 03 - Settings ---------------------------------------------- #+++---var_name_rv <- "_+B+_var_name_rv_+B+_" #+++---var_name_factor <- "_+B+_var_name_factor_+B+_" #+++---alpha_value <- _+B+_alpha_value_+B+_ #+++---vector_ordered_levels <- _+C+_vector_ordered_levels_+C+_ #+++---vector_ordered_colors <- _+C+_vector_ordered_colors_+C+_ # # # # # Section 04 - Standard actions -------------------------------------- # The factor must be factor data type on R. <- as.factor (as.character (my_dataset[,var_name_factor]))#my_dataset[,var_name_factor] <- factor(my_dataset[,var_name_factor], levels = vector_ordered_levels) # # # # # Section 05 - Alpha and confidence value ---------------------------- <- 1 - alpha_value<- data.frame ("order" = 1 : 2 ,"detail" = c ("alpha value" , "confidence value" ),"probability" = c (alpha_value, confidence_value),"percentaje" = paste0 (c (alpha_value, confidence_value)* 100 , "%" )
order detail probability percentaje
1 1 alpha value 0.05 5%
2 2 confidence value 0.95 95%
# # # # # Section 06 - Selected variables and roles ------------------------- <- colnames (my_dataset)<- c (var_name_rv, var_name_factor)<- c ("RV" , "FACTOR" )<- data.frame ("order" = 1 : length (vector_name_selected_vars),"var_name" = vector_name_selected_vars,"var_number" = match (vector_name_selected_vars, vector_all_var_names),"var_letter" = openxlsx:: int2col (match (vector_name_selected_vars, vector_all_var_names)),"var_role" = vector_rol_vars,"doble_reference" = paste0 (vector_rol_vars, "(" , vector_name_selected_vars, ")" )
order var_name var_number var_letter var_role doble_reference
1 1 mpg 1 A RV RV(mpg)
2 2 cyl 2 B FACTOR FACTOR(cyl)
# # # # # Section 05 - minibase ------------------------------------------------ # Only selected variabless. # Only completed rows. # Factor columns as factor object in R. <- na.omit (my_dataset[vector_name_selected_vars])#colnames(minibase) <- vector_rol_vars <- as.factor (minibase[,var_name_factor])head (x = minibase, n = 5 )
mpg cyl
Mazda RX4 21.0 6
Mazda RX4 Wag 21.0 6
Datsun 710 22.8 4
Hornet 4 Drive 21.4 6
Hornet Sportabout 18.7 8
# # # Anova control # 'VR' must be numeric and 'FACTOR must be factor. <- data.frame ("order" = 1 : nrow (df_selected_vars),"var_name" = df_selected_vars$ "var_name" ,"var_role" = df_selected_vars$ "var_role" ,"control" = c ("is.numeric()" , "is.factor()" ),"verify" = c (is.numeric (minibase[,var_name_rv]), is.factor (minibase[,var_name_factor]))
order var_name var_role control verify
1 1 mpg RV is.numeric() TRUE
2 2 cyl FACTOR is.factor() TRUE
# # # my_dataset and minibase reps # Our 'n' is from minibase <- data.frame ("object" = c ("my_dataset" , "minibase" ),"n_col" = c (ncol (my_dataset), ncol (minibase)),"n_row" = c (nrow (my_dataset), nrow (minibase))
object n_col n_row
1 my_dataset 11 32
2 minibase 2 32
# # # Factor info # Default order for levels its alphabetic order. <- data.frame ("order" = 1 : nlevels (minibase[,2 ]),"level" = levels (minibase[,2 ]),"n" = as.vector (table (minibase[,2 ])),"mean" = tapply (minibase[,1 ], minibase[,2 ], mean),"color" = vector_ordered_colorsrownames (df_factor_info) <- 1 : nrow (df_factor_info)
order level n mean color
1 1 4 11 26.66364 #000000
2 2 6 7 19.74286 #00FF00
3 3 8 14 15.10000 #0000FF
# # # Unbalanced reps for levels? # Important information for Tukey. # If reps its equal or not equal in all levels must be detailled # on Tukey. <- length (unique (df_factor_info$ n)) > 1 <- "The design is unbalanced in repetitions. A correction is applied to the Tukey test." <- "The design is unbalanced in replicates. A correction should be applied to the Tukey test." <- ifelse (test = check_unbalanced_reps, yes = phrase_yes_unbalanced,no = phrase_no_unbalanced)
[1] "The design is unbalanced in repetitions. A correction is applied to the Tukey test."
# # # # # Section 06 - Anova Test ---------------------------------------------- # # # Anova test <- paste0 (var_name_rv, " ~ " , var_name_factor)<- as.formula (the_formula)<- lm (formula = the_formula, data = minibase) # Linear model <- aov (lm_anova) # R results for anova <- as.data.frame (summary (aov_anova)[[1 ]]) # Common anova table
Df Sum Sq Mean Sq F value Pr(>F)
cyl 2 824.7846 412.39230 39.69752 4.978919e-09
Residuals 29 301.2626 10.38837 NA NA
# # # Standard error from model for each level <- df_table_anova$ ` Mean Sq ` [2 ]<- sqrt (model_error_var_MSE)<- data.frame ("order" = df_factor_info$ order,"level" = df_factor_info$ level,"n" = df_factor_info$ n,"model_error_var_MSE" = model_error_var_MSE,"model_error_sd" = model_error_sd"model_error_se" ] <- df_model_error["model_error_sd" ]/ sqrt (df_model_error$ n)
order level n model_error_var_MSE model_error_sd model_error_se
1 1 4 11 10.38837 3.223099 0.9718008
2 2 6 7 10.38837 3.223099 1.2182168
3 3 8 14 10.38837 3.223099 0.8614094
# # # # # Section 07 - minibase_mod -------------------------------------------- # # # Detect rows on my_dataset there are on minibase <- rowSums (! is.na (my_dataset[vector_name_selected_vars])) == ncol (minibase)# # # Object minibase_mod and new cols <- minibase$ "lvl_order_number" <- as.numeric (minibase_mod[,2 ])$ "lvl_color" <- df_factor_info$ color[minibase_mod$ "lvl_order_number" ]$ "fitted.values" <- df_factor_info$ "mean" [minibase_mod$ "lvl_order_number" ]$ "residuals" <- lm_anova$ residuals$ "id_my_dataset" <- c (1 : nrow (my_dataset))[dt_rows_my_dataset_ok]$ "id_minibase" <- 1 : nrow (minibase)$ "studres" <- minibase_mod$ "residuals" / model_error_sd# # # # # Section 08 - Requeriments for residuals------------------------------- # # # Normality test (Shapiro-Wilk) <- shapiro.test (minibase_mod$ residuals)
Shapiro-Wilk normality test
data: minibase_mod$residuals
W = 0.97065, p-value = 0.5177
# # # Homogeinidy test (Bartlett) <- paste0 ("residuals" , " ~ " , var_name_factor)<- as.formula (the_formula_bartlett)<- bartlett.test (the_formula_bartlett, data = minibase_mod)
Bartlett test of homogeneity of variances
data: residuals by cyl
Bartlett's K-squared = 8.3934, df = 2, p-value = 0.01505
# # # Residuals variance from levels from original residuals <- data.frame ("order" = 1 : nlevels (minibase_mod[,2 ]),"level" = levels (minibase_mod[,2 ]),"n" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], length),"raw_error_var" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], var),"raw_error_sd" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], sd)"raw_error_se" ] <- df_model_error["model_error_sd" ]/ sqrt (df_model_error$ "n" )rownames (df_raw_error) <- 1 : nrow (df_raw_error)
order level n raw_error_var raw_error_sd
1 1 4 11 20.338545 4.509828
2 2 6 7 2.112857 1.453567
3 3 8 14 6.553846 2.560048
<- c ("Anova and Tukey use MSE from model." , "Bartlett use variance from raw error on each level." ,"Only if there is homogeneity from raw error variances then is a good idea take desition from MSE." )
[1] "Anova and Tukey use MSE from model."
[2] "Bartlett use variance from raw error on each level."
[3] "Only if there is homogeneity from raw error variances then is a good idea take desition from MSE."
# # # Sum for residuals <- sum (minibase_mod$ residuals)# # # Mean for residuals <- mean (minibase_mod$ residuals)################################## <- test_residuals_normality$ p.value<- test_residuals_homogeneity$ p.value<- df_table_anova$ "Pr(>F)" [1 ]<- c (p_value_shapiro, p_value_bartlett, p_value_anova)<- vector_p_value < alpha_value<- ifelse (test = vector_logic_rejected, yes = "Ho Rejected" , "Ho no rejected" )<- ifelse (test = vector_logic_rejected, yes = "Yes" , "No" )<- data.frame ("test" = c ("Shapiro-Wilk test" , "Bartlett test" , "Anova 1 way" ),"aim" = c ("Normality" , "Homogeneity" , "Mean" ),"variable" = c ("residuals" , "residuals" , var_name_rv),"p_value" = vector_p_value,"alpha_value" = c (alpha_value, alpha_value, alpha_value),"Decision" = vector_ho_decision
test aim variable p_value alpha_value
1 Shapiro-Wilk test Normality residuals 5.176650e-01 0.05
2 Bartlett test Homogeneity residuals 1.504518e-02 0.05
3 Anova 1 way Mean mpg 4.978919e-09 0.05
Decision
1 Ho no rejected
2 Ho Rejected
3 Ho Rejected
<- p_value_shapiro < alpha_value<- "The null hypothesis of normal distribution of residuals is rejected." <- "The null hypothesis of normal distribution of residuals is not rejected." <- ifelse (test = check_shapiro_rejected, yes = phrase_shapiro_yes_rejected, no = phrase_shapiro_no_rejected)
[1] "The null hypothesis of normal distribution of residuals is not rejected."
<- p_value_bartlett < alpha_value<- "The hypothesis of homogeneity of variances (homoscedasticity) is rejected." <- "The hypothesis of homogeneity of variances (homoscedasticity) is not rejected." <- ifelse (test = check_bartlett_rejected, yes = phrase_bartlett_yes_rejected, no = phrase_bartlett_no_rejected)
[1] "The hypothesis of homogeneity of variances (homoscedasticity) is rejected."
<- sum (vector_logic_rejected[c (1 ,2 )]) == 2 <- "All residual assumptions are met, so it is valid to draw conclusions from the ANOVA test." <- "Not all model assumptions are met, so it is NOT valid to draw conclusions from the ANOVA test." <- ifelse (test = check_ok_all_requeriments, yes = phrase_requeriments_yes_valid, no = phrase_requeriments_no_valid)
[1] "Not all model assumptions are met, so it is NOT valid to draw conclusions from the ANOVA test."
<- p_value_anova < alpha_value<- "The null hypothesis of the ANOVA test is rejected. There are statistically significant differences in at least one level of the factor." <- "The null hypothesis of the ANOVA test is not rejected. All levels of the factor are statistically equal." <- ifelse (test = check_anova_rejected, yes = phrase_anova_yes_rejected, no = phrase_anova_no_rejected)<- ifelse (test = check_ok_all_requeriments,yes = phrase_anova_selected,no = "Regardless of the p-value obtained in ANOVA, it is not valid to draw conclusions."
[1] "Regardless of the p-value obtained in ANOVA, it is not valid to draw conclusions."
############################################################################## <- agricolae:: HSD.test (y = lm_anova,trt = colnames (minibase_mod)[2 ],alpha = alpha_value,group = TRUE ,console = FALSE ,unbalanced = check_unbalanced_reps)# # # Tukey test - Tukey pairs comparation - Full version <- agricolae:: HSD.test (y = lm_anova,trt = colnames (minibase_mod)[2 ],alpha = alpha_value,group = FALSE ,console = FALSE ,unbalanced = check_unbalanced_reps)# # Original table from R about Tukey <- tukey01_full_groups$ groups
mpg groups
4 26.66364 a
6 19.74286 b
8 15.10000 c
# # # New table about Tukey <- data.frame ("level" = rownames (tukey01_full_groups$ groups),"mean" = tukey01_full_groups$ groups[,1 ],"group" = tukey01_full_groups$ groups[,2 ]
level mean group
1 4 26.66364 a
2 6 19.74286 b
3 8 15.10000 c
# # # # # Section 10 - Partitioned Measures (VR)-------------------------------- # # # Partitioned Measures of Position (VR) <- data.frame ("order" = 1 : nlevels (minibase[,2 ]),"level" = levels (minibase[,2 ]),"min" = tapply (minibase[,1 ], minibase[,2 ], min),"mean" = tapply (minibase[,1 ], minibase[,2 ], mean),"Q1" = tapply (minibase[,1 ], minibase[,2 ], quantile, 0.25 ),"median" = tapply (minibase[,1 ], minibase[,2 ], median),"Q3" = tapply (minibase[,1 ], minibase[,2 ], quantile, 0.75 ),"max" = tapply (minibase[,1 ], minibase[,2 ], max),"n" = tapply (minibase[,1 ], minibase[,2 ], length)# # # Partitioned Measures of Dispersion (VR) <- data.frame ("order" = 1 : nlevels (minibase[,2 ]),"level" = levels (minibase[,2 ]),"range" = tapply (minibase[,1 ], minibase[,2 ], function (x){max (x) - min (x)}),"variance" = tapply (minibase[,1 ], minibase[,2 ], var),"standard_deviation" = tapply (minibase[,1 ], minibase[,2 ], sd),"standard_error" = tapply (minibase[,1 ], minibase[,2 ], function (x){sd (x)/ sqrt (length (x))}),"n" = tapply (minibase[,1 ], minibase[,2 ], length)
order level range variance standard_deviation standard_error n
4 1 4 12.5 20.338545 4.509828 1.3597642 11
6 2 6 3.6 2.112857 1.453567 0.5493967 7
8 3 8 8.8 6.553846 2.560048 0.6842016 14
# # # General Measures of Position (VR) <- data.frame ("min" = min (minibase[,1 ]),"mean" = mean (minibase[,1 ]),"median" = median (minibase[,1 ]),"max" = max (minibase[,1 ]),"n" = length (minibase[,1 ])
min mean median max n
1 10.4 20.09062 19.2 33.9 32
# # # General Measures of Dispersion (VR) <- data.frame ("range" = max (minibase[,1 ]) - min (minibase[,1 ]),"variance" = var (minibase[,1 ]),"standard_deviation" = sd (minibase[,1 ]),"standard_error" = sd (minibase[,1 ])/ (sqrt (length (minibase[,1 ]))),"n" = length (minibase[,1 ])
range variance standard_deviation standard_error n
1 23.5 36.3241 6.026948 1.065424 32
# # # # # Section 11 - Partitioned Measures (Residuals)------------------------- # # # Partitioned Measures of Position (residuals) <- data.frame ("order" = 1 : nlevels (minibase_mod[,2 ]),"level" = levels (minibase_mod[,2 ]),"min" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], min),"mean" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], mean),"median" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], median),"max" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], max),"n" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], length)
order level min mean median max n
4 1 4 -5.263636 -4.037175e-17 -0.66363636 7.236364 11
6 2 6 -1.942857 -7.943233e-18 -0.04285714 1.657143 7
8 3 8 -4.700000 1.193252e-17 0.10000000 4.100000 14
# # # Partitioned Measures of Dispersion (residuals) <- data.frame ("order" = 1 : nlevels (minibase_mod[,2 ]),"level" = levels (minibase_mod[,2 ]),"range" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], function (x){max (x) - min (x)}),"variance" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], var),"standard_deviation" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], sd),"standard_error" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], function (x){sd (x)/ sqrt (length (x))}),"n" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], length)
order level range variance standard_deviation standard_error n
4 1 4 12.5 20.338545 4.509828 1.3597642 11
6 2 6 3.6 2.112857 1.453567 0.5493967 7
8 3 8 8.8 6.553846 2.560048 0.6842016 14
# # # General Measures of Position (residuals) <- data.frame ("min" = min (minibase_mod$ residuals),"mean" = mean (minibase_mod$ residuals),"median" = median (minibase_mod$ residuals),"max" = max (minibase_mod$ residuals),"n" = length (minibase_mod$ residuals)
min mean median max n
1 -5.263636 -1.040834e-17 0.02857143 7.236364 32
# # # General Measures of Dispersion (residuals) <- data.frame ("range" = max (minibase_mod$ residuals) - min (minibase_mod$ residuals),"variance" = var (minibase_mod$ residuals),"standard_deviation" = sd (minibase_mod$ residuals),"standard_error" = sd (minibase_mod$ residuals)/ (sqrt (length (minibase_mod$ residuals))),"n" = length (minibase_mod$ residuals)
range variance standard_deviation standard_error n
1 12.5 9.718148 3.117394 0.5510827 32
# # # # # Section 12 - Model estimators ---------------------------------------- # # # Means for each level <- df_vr_position_levels$ mean
[1] 26.66364 19.74286 15.10000
# # # Mean of means <- mean (vector_est_mu_i)<- rep (est_mu, length (vector_est_mu_i))
[1] 20.50216 20.50216 20.50216
# # # Tau efects <- vector_est_mu_i - vector_est_mu
[1] 6.1614719 -0.7593074 -5.4021645
# # # Sum of tau efects <- sum (vector_est_tau_i)# # # Long model information on dataframe <- data.frame ("order" = df_factor_info$ order,"level" = df_factor_info$ level,"n" = df_factor_info$ n,"est_mu" = vector_est_mu,"est_tau_i" = vector_est_tau_i
order level n est_mu est_tau_i
1 1 4 11 20.50216 6.1614719
2 2 6 7 20.50216 -0.7593074
3 3 8 14 20.50216 -5.4021645
# # # Short model information on dataframe <- data.frame ("order" = df_factor_info$ order,"level" = df_factor_info$ level,"n" = df_factor_info$ n,"est_mu_i" = vector_est_mu_i
order level n est_mu_i
1 1 4 11 26.66364
2 2 6 7 19.74286
3 3 8 14 15.10000
# # # # # Section 13 - Special table to plots ---------------------------------- # # # Table for plot001 <- data.frame ("order" = df_factor_info$ order,"level" = df_factor_info$ level,"n" = df_factor_info$ n,"mean" = tapply (minibase[,1 ], minibase[,2 ], mean),"min" = tapply (minibase[,1 ], minibase[,2 ], min),"max" = tapply (minibase[,1 ], minibase[,2 ], max),"sd" = tapply (minibase[,1 ], minibase[,2 ], sd),"var" = tapply (minibase[,1 ], minibase[,2 ], var)<- data.frame ("order" = df_factor_info$ order,"level" = df_factor_info$ level,"n" = df_factor_info$ n,"mean" = tapply (minibase[,1 ], minibase[,2 ], mean),"model_error_sd" = df_model_error$ model_error_sd"lower_limit" ] <- df_table_factor_plot002$ mean - df_table_factor_plot002$ model_error_sd"upper_limmit" ] <- df_table_factor_plot002$ mean + df_table_factor_plot002$ model_error_sd"color" ] <- df_factor_info$ color
order level n mean model_error_sd lower_limit upper_limmit color
4 1 4 11 26.66364 3.223099 23.44054 29.88674 #000000
6 2 6 7 19.74286 3.223099 16.51976 22.96596 #00FF00
8 3 8 14 15.10000 3.223099 11.87690 18.32310 #0000FF
<- data.frame ("order" = df_factor_info$ order,"level" = df_factor_info$ level,"n" = df_factor_info$ n,"mean" = tapply (minibase[,1 ], minibase[,2 ], mean),"model_error_se" = df_model_error$ model_error_se"lower_limit" ] <- df_table_factor_plot003$ mean - df_table_factor_plot003$ model_error_se"upper_limmit" ] <- df_table_factor_plot003$ mean + df_table_factor_plot003$ model_error_se"color" ] <- df_factor_info$ color
order level n mean model_error_se lower_limit upper_limmit color
4 1 4 11 26.66364 0.9718008 25.69184 27.63544 #000000
6 2 6 7 19.74286 1.2182168 18.52464 20.96107 #00FF00
8 3 8 14 15.10000 0.8614094 14.23859 15.96141 #0000FF
# # # Table for plot004 <- df_vr_position_levels"color" ] <- df_factor_info$ color# # # Table for plot005 <- df_table_factor_plot004# # # Table for plot006 <- df_table_factor_plot004<- df_table_factor_plot003<- order (df_tukey_table$ level)<- df_tukey_table$ group[correct_pos_letters]"group" ] <- vector_letters# # # Table for plot006 <- data.frame ("order" = 1 : nlevels (minibase_mod[,2 ]),"level" = levels (minibase_mod[,2 ]),"n" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], length),"min" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], min),"mean" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], mean),"max" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], max),"var" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], var),"sd" = tapply (minibase_mod$ residuals, minibase_mod[,2 ], sd),"color" = df_factor_info$ color
order level n min mean max var sd color
4 1 4 11 -5.263636 -4.037175e-17 7.236364 20.338545 4.509828 #000000
6 2 6 7 -1.942857 -7.943233e-18 1.657143 2.112857 1.453567 #00FF00
8 3 8 14 -4.700000 1.193252e-17 4.100000 6.553846 2.560048 #0000FF
# # # Table for plot006 <- df_table_residuals_plot001# # # Table for plot006 <- df_table_residuals_plot001# # # Table for plot006 <- data.frame ("variable" = "residuals" ,"n" = length (minibase_mod$ residuals),"min" = min (minibase_mod$ residuals),"mean" = mean (minibase_mod$ residuals),"max" = max (minibase_mod$ residuals),"var" = var (minibase_mod$ residuals),"sd" = sd (minibase_mod$ residuals),"model_error_var_MSE" = model_error_var_MSE,"model_error_sd" = model_error_sd<- "Model Error (MSE) " # # # Table for plot006 <- df_table_residuals_plot004# # # Table for plot006 <- data.frame ("order" = 1 : nlevels (minibase_mod[,2 ]),"level" = levels (minibase_mod[,2 ]),"n" = tapply (minibase_mod$ studres, minibase_mod[,2 ], length),"min" = tapply (minibase_mod$ studres, minibase_mod[,2 ], min),"mean" = tapply (minibase_mod$ studres, minibase_mod[,2 ], mean),"max" = tapply (minibase_mod$ studres, minibase_mod[,2 ], max),"var" = tapply (minibase_mod$ studres, minibase_mod[,2 ], var),"sd" = tapply (minibase_mod$ studres, minibase_mod[,2 ], sd),"color" = df_factor_info$ color# # # Table for plot006 <- df_table_residuals_plot006<- data.frame ("variable" = "studres" ,"n" = length (minibase_mod$ studres),"min" = min (minibase_mod$ studres),"mean" = mean (minibase_mod$ studres),"max" = max (minibase_mod$ studres),"var" = var (minibase_mod$ studres),"sd" = sd (minibase_mod$ studres)<- df_table_residuals_plot008<- df_table_residuals_plot008############################################################# # # # Create a new plot... <- plotly:: plot_ly ()# # # Plot001 - Scatter plot for VR and FACTOR on minibase_mod ***************** <- plotly:: add_trace (p = plot001_factor,type = "scatter" ,mode = "markers" ,x = minibase_mod[,var_name_factor],y = minibase_mod[,var_name_rv],color = minibase_mod[,var_name_factor],colors = df_factor_info$ color,marker = list (size = 15 , opacity = 0.7 ))# # # Title and settings... <- plotly:: layout (p = plot001_factor,title = "Plot 001 - Scatterplot" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines <- plotly:: layout (p = plot001_factor,xaxis = list (zeroline = FALSE ),yaxis = list (zeroline = FALSE ))# # # Plot output ############################################################################## # # # Create a new plot... <- plot_ly ()# # # Adding errors... <- add_trace (p = plot002_factor,type = "scatter" ,mode = "markers" ,x = df_table_factor_plot002$ level,y = df_table_factor_plot002$ mean,color = df_table_factor_plot002$ level,colors = df_table_factor_plot002$ color,marker = list (symbol = "line-ew-open" ,size = 50 ,opacity = 1 ,line = list (width = 5 )),error_y = list (type = "data" , array = df_table_factor_plot002$ model_error_sd)# # # Title and settings... <- plotly:: layout (p = plot002_factor,title = "Plot 002 - Mean and model standard deviation" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines <- plotly:: layout (p = plot002_factor,xaxis = list (zeroline = FALSE ),yaxis = list (zeroline = FALSE ))# # # Plot output ############################################################################## # # # Create a new plot... <- plotly:: plot_ly ()# # # Adding errors... <- plotly:: add_trace (p = plot003_factor,type = "scatter" ,mode = "markers" ,x = df_table_factor_plot003$ level,y = df_table_factor_plot003$ mean,color = df_table_factor_plot003$ level,colors = df_table_factor_plot003$ color,marker = list (symbol = "line-ew-open" ,size = 50 ,opacity = 1 ,line = list (width = 5 )),error_y = list (type = "data" , array = df_table_factor_plot003$ model_error_se)# # # Title and settings... <- plotly:: layout (p = plot003_factor,title = "Plot 003 - Mean y model standard error" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines <- plotly:: layout (p = plot003_factor,xaxis = list (zeroline = FALSE ),yaxis = list (zeroline = FALSE ))# # # Plot output ############################################################################## # # # New plotly... <- plotly:: plot_ly ()# # # Boxplot and info... <- plotly:: add_trace (p = plot004_factor,type = "box" ,x = df_table_factor_plot004$ level ,color = df_table_factor_plot004$ level,colors = df_table_factor_plot004$ color,lowerfence = df_table_factor_plot004$ min,q1 = df_table_factor_plot004$ Q1,median = df_table_factor_plot004$ median,q3 = df_table_factor_plot004$ Q3,upperfence = df_table_factor_plot004$ max,boxmean = TRUE ,boxpoints = FALSE ,line = list (color = "black" , width = 3 )# # # Title and settings... <- plotly:: layout (p = plot004_factor,title = "Plot 004 - Boxplot and means" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines... <- plotly:: layout (p = plot004_factor,xaxis = list (zeroline = FALSE ),yaxis = list (zeroline = FALSE ))# # # Output plot004_anova... ############################################################################## <- levels (minibase_mod[,2 ])<- length (all_levels)<- rainbow (length (all_levels))<- plot_ly ()# Violinplot for (k in 1 : n_levels){# Selected values <- all_levels[k]<- all_color[k]<- minibase_mod[,2 ] == selected_level# Plotting selected violinplot <- plot005_factor %>% add_trace (x = minibase_mod[,2 ][dt_filas],y = minibase_mod[,1 ][dt_filas],type = "violin" ,name = paste0 ("violin" , k),points = "all" ,marker = list (color = selected_color),line = list (color = selected_color),fillcolor = I (selected_color)# Boxplot <- plotly:: add_trace (p = plot005_factor,type = "box" ,name = "boxplot" ,x = df_table_factor_plot005$ level ,color = df_table_factor_plot005$ level ,lowerfence = df_table_factor_plot005$ min,q1 = df_table_factor_plot005$ Q1,median = df_table_factor_plot005$ median,q3 = df_table_factor_plot005$ Q3,upperfence = df_table_factor_plot005$ max,boxmean = TRUE ,boxpoints = TRUE ,fillcolor = df_table_factor_plot005$ color,line = list (color = "black" , width = 3 ),opacity = 0.5 ,width = 0.2 )# # # Title and settings... <- plotly:: layout (p = plot005_factor,title = "Plot 005 - Violinplot" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines... <- plotly:: layout (p = plot005_factor,xaxis = list (zeroline = FALSE ),yaxis = list (zeroline = FALSE ))# # # Output plot003_anova... ############################################################################## #library(plotly) <- plotly:: plot_ly ()# Add traces <- plotly:: add_trace (p = plot006_factor,type = "violin" ,y = minibase_mod$ VR,x = minibase_mod$ FACTOR,showlegend = TRUE ,side = "positive" ,points = "all" ,name = "Violinplot" ,color = minibase_mod$ FACTOR,colors = df_table_factor_plot006$ color)# # # Title and settings... <- plotly:: layout (p = plot006_factor,title = "Plot 006 - Scatterplot + Jitter + Smoothed" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines... <- plotly:: layout (p = plot006_factor,xaxis = list (zeroline = FALSE ),yaxis = list (zeroline = FALSE ))# # # Output plot003_anova... ############################################################################## # # # Create a new plot... <- plotly:: plot_ly ()# # # Adding errors... <- plotly:: add_trace (p = plot007_factor,type = "scatter" ,mode = "markers" ,x = df_table_factor_plot007$ level,y = df_table_factor_plot007$ mean,color = df_table_factor_plot007$ level,colors = df_table_factor_plot007$ color,marker = list (symbol = "line-ew-open" ,size = 50 ,opacity = 1 ,line = list (width = 5 )),error_y = list (type = "data" , array = df_table_factor_plot007$ model_error_se)<- add_text (p = plot007_factor,x = df_table_factor_plot007$ level,y = df_table_factor_plot007$ mean,text = df_table_factor_plot007$ group, name = "Tukey Group" ,size = 20 )# # # Title and settings... <- plotly:: layout (p = plot007_factor,title = "Plot 007 - Mean y model standard error" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines <- plotly:: layout (p = plot007_factor,xaxis = list (zeroline = FALSE ),yaxis = list (zeroline = FALSE ))# # # Plot output #######---- ####### DESDE ACAAAAAAAAAAAAAAAAAAAA # # # Create a new plot... <- plotly:: plot_ly ()# # # Plot001 - Scatter plot for VR and FACTOR on minibase_mod ***************** <- plotly:: add_trace (p = plot001_residuals,type = "scatter" ,mode = "markers" ,x = minibase_mod$ FACTOR,y = minibase_mod$ residuals,color = minibase_mod$ FACTOR,colors = df_factor_info$ color,marker = list (size = 15 , opacity = 0.7 ))# # # Title and settings... <- plotly:: layout (p = plot001_residuals,title = "Plot 001 - Scatterplot - Residuals" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines <- plotly:: layout (p = plot001_residuals,xaxis = list (zeroline = FALSE ),yaxis = list (zeroline = TRUE ))# # # Plot output #library(plotly) <- plotly:: plot_ly ()# Add traces <- plotly:: add_trace (p = plot002_residuals,type = "violin" ,y = minibase_mod$ residuals,x = minibase_mod$ FACTOR,showlegend = TRUE ,side = "positive" ,points = "all" ,name = "Violinplot" ,color = minibase_mod$ FACTOR,colors = df_table_residuals_plot002$ color)# # # Title and settings... <- plotly:: layout (p = plot002_residuals,title = "Plot 002 - Residuals - Scatterplot + Jitter + Smoothed" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines... <- plotly:: layout (p = plot002_residuals,xaxis = list (zeroline = FALSE ),yaxis = list (zeroline = FALSE ))# # # Output plot003_anova... <- plotly:: plot_ly ()# Add traces <- plotly:: add_trace (p = plot003_residuals,type = "violin" ,x = minibase_mod$ residuals,showlegend = TRUE ,side = "positive" ,points = FALSE ,#name = levels(minibase_mod$FACTOR)[minibase_mod$lvl_order_number], color = minibase_mod$ FACTOR,colors = df_table_residuals_plot003$ color)# # # Title and settings... <- plotly:: layout (p = plot003_residuals,title = "Plot 003 - Residuals - Scatterplot + Jitter + Smoothed" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines... <- plotly:: layout (p = plot003_residuals,xaxis = list (zeroline = FALSE ),yaxis = list (zeroline = FALSE ))# # # Output plot003_anova... <- plotly:: plot_ly ()# Add traces <- plotly:: add_trace (p = plot004_residuals,type = "violin" ,x = minibase_mod$ residuals,#x = minibase_mod$FACTOR, showlegend = TRUE ,side = "positive" ,points = "all" ,name = " " )# #color = minibase_mod$FACTOR, #colors = df_table_factor_plot006$color) # # # Title and settings... <- plotly:: layout (p = plot004_residuals,title = "Plot 004 - Residuals" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines... <- plotly:: layout (p = plot004_residuals,xaxis = list (zeroline = TRUE ),yaxis = list (zeroline = FALSE ))# # # Output plot003_anova... # - el 5 <- EnvStats:: qqPlot (x = minibase_mod$ residuals, plot.it = F,param.list = list (mean = mean (minibase_mod$ residuals),sd = sd (minibase_mod$ residuals)))<- qq_info$ x<- qq_info$ y#library(plotly) <- plotly:: plot_ly ()# Crear el gráfico QQ plot <- add_trace (p = plot005_residuals,x = cuantiles_teoricos,y = cuantiles_observados,type = 'scatter' , mode = 'markers' ,marker = list (color = 'blue' ),name = "points" )# Agregar la línea de identidad <- 1 <- 0 # Calcular las coordenadas de los extremos de la línea de identidad <- range (cuantiles_teoricos)<- pendiente * x_extremos + intercepto# Agregar la recta de identidad <- add_trace (p = plot005_residuals,x = x_extremos,y = y_extremos,type = 'scatter' ,mode = 'lines' ,line = list (color = 'red' ),name = "identity" )# Establecer etiquetas de los ejes # plot007_residuals <- layout(p = plot007_residuals, # xaxis = list(title = 'Expected quantiles'), # yaxis = list(title = 'Observed quantiles')) <- plotly:: layout (p = plot005_residuals,title = "Plot 005 - QQ Plot Residuals" ,font = list (size = 20 ),margin = list (t = 100 ))# Mostrar el gráfico # - Fin el 5 # # # Create a new plot... <- plotly:: plot_ly ()# # # Plot001 - Scatter plot for VR and FACTOR on minibase_mod ***************** <- plotly:: add_trace (p = plot006_residuals,type = "scatter" ,mode = "markers" ,x = minibase_mod$ fitted.values,y = minibase_mod$ residuals,color = minibase_mod$ FACTOR,colors = df_factor_info$ color,marker = list (size = 15 , opacity = 0.7 ))# # # Title and settings... <- plotly:: layout (p = plot006_residuals,title = "Plot 006 - Scatterplot - Residuals vs Fitted.values" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines <- plotly:: layout (p = plot006_residuals,xaxis = list (zeroline = FALSE ),yaxis = list (zeroline = TRUE ))# # # Plot output # # # Create a new plot... <- plotly:: plot_ly ()# # # Plot001 - Scatter plot for VR and FACTOR on minibase_mod ***************** <- plotly:: add_trace (p = plot007_residuals,type = "scatter" ,mode = "markers" ,x = minibase_mod$ FACTOR,y = minibase_mod$ studres,color = minibase_mod$ FACTOR,colors = df_factor_info$ color,marker = list (size = 15 , opacity = 0.7 ))# # # Title and settings... <- plotly:: layout (p = plot007_residuals,title = "Plot 007 - Scatterplot - Studentized Residuals" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines <- plotly:: layout (p = plot007_residuals,xaxis = list (zeroline = FALSE ),yaxis = list (zeroline = TRUE ))# # # Plot output #library(plotly) <- plotly:: plot_ly ()# Add traces <- plotly:: add_trace (p = plot008_residuals,type = "violin" ,x = minibase_mod$ studres,#x = minibase_mod$FACTOR, showlegend = TRUE ,side = "positive" ,points = "all" ,name = " " )# #color = minibase_mod$FACTOR, #colors = df_table_factor_plot006$color) # # # Title and settings... <- plotly:: layout (p = plot008_residuals,title = "Plot 008 - Studentized Residuals" ,font = list (size = 20 ),margin = list (t = 100 ))# # # Without zerolines... <- plotly:: layout (p = plot008_residuals,xaxis = list (zeroline = TRUE ),yaxis = list (zeroline = FALSE ))# # # Output plot003_anova... # el 9 <- seq (- 4 , 4 , length.out = 100 )<- dnorm (x, mean = 0 , 1 )# x <- x*model_error_sd <- density (x, kernel = "gaussian" , adjust = 0.5 )<- hist (minibase_mod$ studres, plot = FALSE )$ "rel_frec" <- hist_data_studres$ counts/ sum (hist_data_studres$ counts)<- density (x = minibase_mod$ studres, kernel = "gaussian" , adjust = 0.5 )#library(plotly) <- plotly:: plot_ly ()# plot005_residuals <- add_trace(p = plot005_residuals, # x = densidad_studres$x, # y = densidad_studres$y, # type = 'scatter', # mode = 'lines', # name = 'densidad_studres') <- add_trace (p = plot009_residuals,x = x,y = y,type = 'scatter' ,mode = 'lines' ,name = 'Normal Standard' )# # Add traces # plot005_residuals <- plotly::add_trace(p = plot005_residuals, # type = "violin", # x = minibase_mod$residuals, # #x = minibase_mod$FACTOR, # showlegend = TRUE, # side = "positive", # points = FALSE, # name = "violinplot")# <- plotly:: add_trace (p = plot009_residuals,type = "bar" ,x = hist_data_studres$ "mids" ,y = hist_data_studres$ "density" ,name = "hist - studres" )<- plotly:: layout (p = plot009_residuals,bargap = 0 )<- plotly:: layout (p = plot009_residuals,title = "Plot 009 - Studres Distribution" ,font = list (size = 20 ),margin = list (t = 100 ))# fin el 9 <- EnvStats:: qqPlot (x = minibase_mod$ studres, plot.it = F,param.list = list (mean = 0 ,sd = 1 ))<- qq_info$ x<- qq_info$ y#library(plotly) <- plotly:: plot_ly ()# Crear el gráfico QQ plot <- add_trace (p = plot010_residuals,x = cuantiles_teoricos,y = cuantiles_observados,type = 'scatter' , mode = 'markers' ,marker = list (color = 'blue' ),name = "points" )# Agregar la línea de identidad <- 1 <- 0 # Calcular las coordenadas de los extremos de la línea de identidad <- range (cuantiles_teoricos)<- pendiente * x_extremos + intercepto# Agregar la recta de identidad <- add_trace (p = plot010_residuals,x = x_extremos,y = y_extremos,type = 'scatter' ,mode = 'lines' ,line = list (color = 'red' ),name = "identity" )# Establecer etiquetas de los ejes # plot007_residuals <- layout(p = plot007_residuals, # xaxis = list(title = 'Expected quantiles'), # yaxis = list(title = 'Observed quantiles')) <- plotly:: layout (p = plot010_residuals,title = "Plot 010 - QQ Plot - studres" ,font = list (size = 20 ),margin = list (t = 100 ))# Mostrar el gráfico